# Leveraging translations for speech transcription in low-resource settings

arXiv: https://arxiv.org/abs/1803.08991 · HTML: https://mlsanity.com/paper/1803.08991/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2018.

## Built on by (most cited first, 5 of 5)

- [Multiple Sources are Better Than One: Incorporating External Knowledge in Low-Resource Glossing](https://mlsanity.com/paper/2406.11085/) (2024)
- [Breaking the Transcription Bottleneck: Fine-tuning ASR Models for Extremely Low-Resource Fieldwork Languages](https://mlsanity.com/paper/2506.17459/) (2025)
- [Unsupervised Word Segmentation from Discrete Speech Units in Low-Resource Settings](https://mlsanity.com/paper/2106.04298/) (2021)
- [Embedded Translations for Low-resource Automated Glossing](https://mlsanity.com/paper/2403.08189/) (2024)
- [Noisy Parallel Data Alignment](https://mlsanity.com/paper/2301.09685/) (2023)

## Abstract (arXiv)

Recently proposed data collection frameworks for endangered language documentation aim not only to collect speech in the language of interest, but also to collect translations into a high-resource language that will render the collected resource interpretable. We focus on this scenario and explore whether we can improve transcription quality under these extremely low-resource settings with the assistance of text translations. We present a neural multi-source model and evaluate several variations of it on three low-resource datasets. We find that our multi-source model with shared attention outperforms the baselines, reducing transcription character error rate by up to 12.3%.